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A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?

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Author Info

  • Chin-Shien Lin

    (National Chung Hsing University)

  • Haider A. Khan

    (GIGS, University of Denver)

  • Ying-Chieh Wang

    (Providence University)

  • Ruei-Yuan Chang

    (Providence University)

Abstract

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

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Bibliographic Info

Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-411.

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Length: 33pages
Date of creation: Apr 2006
Date of revision:
Handle: RePEc:tky:fseres:2006cf411

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Cited by:
  1. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
  2. Zeynep KARACOR & Korhan GOKMENOGLU, 2012. "Predictability Of Financial Crises: Testing K.R.L. Model In The Case Of Turkey," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 2, pages 5-16, June.

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